Test LLM-integrated applications against known prompt injection techniques, evasion methods, and attack intents using the Arcanum PI Taxonomy. Use when red-teaming AI apps, validating guardrails, or deepening LLM01 (Prompt Injection) assessments.
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Systematically test an LLM application's prompt injection defenses by following the full procedure in plays/prompt-injection-testing.md.
Based on the Arcanum PI Taxonomy by Jason Haddix (Arcanum Information Security). CC BY 4.0.
Scope and Input Surface Mapping — Identify all paths where attacker-controlled content reaches the LLM: direct (chat, API params) and indirect (file uploads, web fetches, RAG docs, tool outputs, MCP resources).
Test by Attack Intent (13 intents) — For each authorized intent, attempt to achieve the attacker's goal:
Test by Attack Technique (18 techniques) — Apply known payload construction methods:
Apply Evasion Layers (20 evasions) — When techniques are blocked, retry with obfuscation:
Execute Test Matrix — Combine intents x techniques x evasions. Prioritize: high-impact intents first, indirect surfaces second, evasion sweeps against defenses that blocked direct attempts.
Assess Results — For each successful injection, document: severity, attack path (intent + technique + evasion + surface), exact payload, detection gap, and remediation.
Defense Validation — Check the 5-layer defense checklist: ecosystem hardening, model guardrails, prompt-layer defenses, data-layer controls, application-layer validation.
Test results summary table (intent / technique / evasion / surface / result / severity), detailed findings using templates/finding.md, defense coverage checklist with gaps highlighted, and prioritized recommendations.
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